Argumentation for machine learning: a survey

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future

The “Logic” of Informal Logic

Are there any logical norms for argument evaluation besides soundness and inductive strength? The paper will look at several concepts or models introduced over the years, including those of Wisdom, Toulmin, Wellman, Rescher, defeasible reasoning proponents and Walton to consider whether there is common ground among them that supplies an alternative to deductive validity and inductive strength

A probabilistic analysis of argument cogency

This paper offers a probabilistic treatment of the conditions for argument cogency as endorsed in informal logic: acceptability, relevance, and sufficiency. Treating a natural language argument as a reason-claim-complex, our analysis identifies content features of defeasible argument on which the RSA conditions depend, namely: change in the commitment to the reason, the reason’s sensitivity and selectivity to the claim, one’s prior commitment to the claim, and the contextually determined thresholds of acceptability for reasons and for claims. Results contrast with, and may indeed serve to correct, the informal understanding and applications of the RSA criteria concerning their conceptual dependence, their function as update-thresholds, and their status as obligatory rather than permissive norms, but also show how these formal and informal normative approachs can in fact align

What types of arguments are there?

Our typology is based on two ground adequacy factors, one logical and one epistemic. Logically, the step from premises to conclusion may be conclusive or only ceteris paribus. Epistemically, warrants may be backed a priori or a posteriori. Hence there are four types of arguments: conclusive a priori, defeasible a priori, defeasible a posteriori, and prima facie conclusive a posteriori. We shall give an example of each and compare our scheme with other typologies

The Toulmin Model and Non-monotonic Reasoning

While the nature of warrants is unclear in both Toulmin’s Uses of Argument and in textbook pedagogy based on it, the theory of non-monotonic reasoning could clarify and enhance our understanding of warrants

Logic of Non-Monotonic Interactive Proofs (Formal Theory of Temporary Knowledge Transfer)

We propose a monotonic logic of internalised non-monotonic or instant interactive proofs (LiiP) and reconstruct an existing monotonic logic of internalised monotonic or persistent interactive proofs (LiP) as a minimal conservative extension of LiiP. Instant interactive proofs effect a fragile epistemic impact in their intended communities of peer reviewers that consists in the impermanent induction of the knowledge of their proof goal by means of the knowledge of the proof with the interpreting reviewer: If my peer reviewer knew my proof then she would at least then (in that instant) know that its proof goal is true. Their impact is fragile and their induction of knowledge impermanent in the sense of being the case possibly only at the instant of learning the proof. This accounts for the important possibility of internalising proofs of statements whose truth value can vary, which, as opposed to invariant statements, cannot have persistent proofs. So instant interactive proofs effect a temporary transfer of certain propositional knowledge (knowable ephemeral facts) via the transmission of certain individual knowledge (knowable non-monotonic proofs) in distributed systems of multiple interacting agents.Comment: continuation of arXiv:1201.3667 ; published extended abstract: DOI:10.1007/978-3-642-36039-8_16 ; related to arXiv:1208.591

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Argumentation for Knowledge Representation, Conflict Resolution, Defeasible Inference and Its Integration with Machine Learning

Modern machine Learning is devoted to the construction of algorithms and computational procedures that can automatically improve with experience and learn from data. Defeasible argumentation has emerged as sub-topic of artificial intelligence aimed at formalising common-sense qualitative reasoning. The former is an inductive approach for inference while the latter is deductive, each one having advantages and limitations. A great challenge for theoretical and applied research in AI is their integration. The first aim of this chapter is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning. It then describes argumentation theory, a paradigm for implementing defeasible reasoning in practice as well as the common multi-layer schema upon which argument-based systems are usually built. The second aim is to describe a selection of argument-based applications in the medical and health-care sectors, informed by the multi-layer schema. A summary of the features that emerge from the applications under review is aimed at showing why defeasible argumentation is attractive for knowledge-representation, conflict resolution and inference under uncertainty. Open problems and challenges in the field of argumentation are subsequently described followed by a future outlook in which three points of integration with machine learning are proposed